to transfer the target style,
where \(\mathbf {x}_c\) , \(\mathbf {x}_s\)  and \(\mathbf {x}\)  are the content, the style and the generated signals, respectively;
\(\mathcal {L}_c(\mathbf {x},\mathbf {x}_c)=\sum _{\ell \in \mathcal {C}} \Vert F_\ell (\mathbf {x})-F_\ell (\mathbf {x}_c)\Vert _2^2/N_\ell \) 
and \(\mathcal {L}_s(\mathbf {x},\mathbf {x}_s)=\sum _{\ell \in \mathcal {S}} \Vert G_\ell (\mathbf {x})-G_\ell (\mathbf {x}_s)\Vert _F^2/N_\ell \) 
are the content and style loss, respectively;
\(\alpha >0\)  controls how much penalty will be exercised over the deviation of the generated audio from the content loss,
\(\mathcal {C}\)  and \(\mathcal {S}\)  are the indices for content and style layers.
The size of \(\mathcal {S}\)  varies between different neural network architectures.
For example, \(|\mathcal {S}|=5\)  in VGG-16, \(|\mathcal {S}|=8\)  in SoundNet[1]} and
\(|\mathcal {S}|=1\)  in the wide-shallow-random network by Ulyanov and Lebedev[2]}.
In our simulation, we have \(|\mathcal {S}|=1\) .
